Along with the increasing number of Covid-19 cases, the development of false news or misinformation about Covid-19 -19 is getting bigger. This article aims to analyze public opinion about the various hoaxes that were widely spread in Indonesia during the pandemic. The method used is a mixture, namely literature review, in the form of searching for related journals regarding the distribution of hoaxes during the pandemic and conducting online surveys via a google form. The research conducted indicates that during the pandemic there were rapid spreads of fake news, it is proven with more than 45% of the participants who were often heard hoax news about Covid-19 on online media. From this evidence, it also can be discovered that hoax news can affect a person's belief in the Covid-19 virus.

Diabetes mellitus is a popular life-threatening disease and patients may gradually have started suffering from other diabetes-causing diseases such as heart attacks, stroke, hypertension, blurry vision, blindness, foot ulcer, amputation, kidney damage and other organ failures before diagnosis. Early detection can help reduce the fatality of this disease. Deep learning models have proven very useful in disease detection and computer-aided diagnosis. In this work, we proposed a deep unsupervised machine learning model for early detection of diabetes using voting ensemble feature selection and deep belief neural networks (DBN). Dataset was obtained from an online repository containing responses of prediagnosed patients to direct questionnaires administered in Sylhet Diabetes Hospital in Sylhet, Bangladesh. The dataset was preprocessed and preprocessed. Features were reduced using the ensemble feature selector. The DBN model was pretrained and tuned to obtain optimal performance. The model was also compared with other models with no multiple hidden layers. The DBN performed at its relative best with F1-measure, precision and recall of 1.00, 0.92 and 1.00 respectively. We conclude that DBN is a useful tool for an unsupervised early prediction of Type II diabetes mellitus.

Abstract Aim: To determine the efficacy of health beliefs model –based intervention in changing the belief related to substance use among university student in Mosul city-Iraq. Design: A randomized controlled trial. Methods: A probability (simple random sample) of (N=80) undergraduate student in different specialties would be selected. The study sample will be recruited from (4) colleges in the University of Mosul's Engineering, Sciences, Medicine and Education Colleges. The sample will be randomly assigned into experimental and control groups of (40) undergraduate student for each group. Such chosen is employed of pool of topics that have the criteria contain students who have using on (Smoking, Hookah, Drug abuse and Alcohol).For during from 25of October / 2019 till 1 of February/2021. Data is analyzed using the "Statistical Package for Social Science" (SPSS) software for Windows (V:26). Results: This finding indicated that before the intervention, mean scores for all concepts of HBM, add to Motivation, Control, and behaviors intensions of students they were almost equal. However, after the intervention were significantly different in the study group, while it was not significant in the control group. Conclusion:This study concluded that designing an HBM-based study could affect students' understanding and their behaviors in the field of substance abuse. Considering the positive correlation between construct of HBM, particularly in "perceived benefits and perceived severity" related to students’ beliefs. These beliefs implied a significant correlation with each other and with the attention to the prevention of addiction.

Background: Renewed measles outbreaks in recent years indicate that despite the routine availability of vaccines for a disease that is considered contagious, dangerous and deadly, many anti-vaccinationists do not vaccinate their children, which consequently endangers public health. This study aimed to investigate the factors that influence mothers to vaccinate their children, and whether the Health Belief Model (HBM) could predict compliance or non-compliance. Methods: This was a quantitative correlational research, utilizing a 40-item questionnaire administered to 181 mothers in Israel. Results: The findings indicated two main factors that affected mothers' intention to vaccinate their children against measles: first, their perception of the vaccine's advantages, and second, their perception of the severity of the disease. It was also found that the HBM variables significantly affected the intention to administer vaccines. Conclusion: Consequently, raising public awareness of the vaccine's advantages and importance to preventing mass infection, as well as attempts by the health system and practitioners to understand the motivations of anti-vaccinationists (including health beliefs and cultural sensitivities) could significantly increase the percentage of vaccinated children, and eradicate the measles epidemic.

Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. Proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission cost, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy.

Malignant growth is the most widely recognized repulsive infections winning around the world, and the patients with disease are saved just when the malignant growth is distinguished at the beginning phase. Each kind of disease is interesting, with its own arrangement of development properties and hereditary changes. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. The general technique of the created approach includes four stages, such as pre-processing, division, highlight extraction, and the order. From the outset, the CT picture of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological and measurable and surface highlights like LOOP and LGP. At long last, the extricated highlights are given to the order step. Here, the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumor, giving two classes in particular, knob or non-knob. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specific, precision, affectability, and the explicitness.

Factorization reduces computational complexity and is therefore an important tool in statistical machine learning of high dimensional systems. Conventional molecular modeling, including molecular dynamics and Monte Carlo simulations of molecular systems, is a large research field based on approximate factorization of molecular interactions. Recently, the local distribution theory was proposed to factorize global joint distribution of a given molecular system into trainable local distributions. Belief propagation algorithms are a family of exact factorization algorithms for trees and are extended to approximate loopy belief propagation algorithms for graphs with loops. Despite the fact that factorization of probability distribution is their common foundation, computational research in molecular systems and machine learning studies utilizing belief propagation algorithms have been carried out independently with respective track of algorithm development. The connection and differences among these factorization algorithms are briefly presented in this perspective, with the hope to intrigue further development in factorization algorithms for physical modeling of complex molecular systems.

Understanding the strength of the associations between perceived risk and individuals’ behavioral intentions to protect their health is important for determining appropriate risk communication strategies in communities impacted by lead contamination. We conducted a survey within communities of northern Idaho, USA (n = 306) near a Superfund megasite with legacy mining contamination. We empirically test a theoretical model based on the Health Belief Model. Survey respondents had higher intentions to practice health protective behaviors when they perceived the risk of lead contamination as severe, recognized the benefits of health protective behaviors, and considered the risks of lead contamination. Women reported higher behavioral intentions than men, but age and mining affiliation did not have an association. Survey comments indicated that perceptions about the long-term environmental remediation in the region influenced risk perceptions. Understanding risk perceptions, behavioral intentions, and related factors can aid public health agencies in tailoring risk communication for increasing protective behaviors in mining-impacted communities internationally.

We have surveyed peer-reviewed literature on the awareness of human papillomavirus (HPV) infection among Indigenous males in North America and Oceania. Using keywords HPV plus male, men or boy, and ethnical filters such as Indigenous, Aboriginal or First Nations, we retrieved 47 articles based on titles of which we kept 14 after reading the abstracts. Reported HPV awareness was generally low in Indigenous males in North America with no peer-reviewed data from Oceania. The lower understanding by males compared to females was largely attributable to misconceptions about HPV-related diseases, their transmission, and prevention. Lack of awareness and concern toward the risk of contracting HPV infection in Indigenous males suggests an impediment in disseminating health information about this cancer-causing virus. Culturally sensitive education, with emphasis on Indigenous males, is needed to improve this group’s HPV knowledge. Researchers should also engage meaningfully with Indigenous communities by building rapport to achieve a positive change in attitude.

Multi-Faith Spaces (MFS) are a relatively recent invention that quickly gained in significance. On the one hand, they offer a convenient solution for satisfying needs of people with diverse beliefs in the institutional context of hospitals, schools, airports, etc. On the other hand, as Andrew Crompton pointed out, they are politically significant because the multi-faith paradigm “is replacing Christianity as the face of public religion in Europe” (2012, p. 493). Due to their ideological entanglement, MFS are often used as the means to promote either a more privatised version of religion, or a certain denominational preference. Two distinct designs are used to achieve these means: negative in the case of the former, and positive in the latter. Neither is without problems, and neither adequately fulfils its primary purpose of serving diverse groups of believers. Both, however, seem to follow the biases and main problems of secularism. In this paper, I analyse recent developments of MFS to detail their main problems and answer the question, whether the MFS, and the underlying Multi-Faith Paradigm, can be classified as a continuation of secularism.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

Oscillometric blood pressure (BP) devices currently estimate a single point but do not identify fluctuations in BP or distinguish them from variations in response to physiological properties. In this paper, to analyze BP normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep neural network (DNN) to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed DNN regression model decreases the standard deviation of error (SDE) of the mean error (ME) and the mean absolute error (MAE) and reduces the uncertainty of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation distribution which fits the Gaussian distribution very well. We use a rank test in the DNN regression model to demonstrate the independence of the artificial SBP and DBP estimations. First, we perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs based on the DNN regression estimator.

To both survive and develop continuously, enterprises must overcome many kinds of competition and challenges. Cultivating employees' active and sustainable organizational citizenship behavior is important for enterprises to successfully cope with turbulence and uncertain events during their development. In this study, we investigated the development level of and factors influencing employees' organizational citizenship behavior in current organizations. By using the Belief in a Just World Scale, Organizational Citizenship Behavior Scale, and Interpersonal Intelligence Scale, we investigated 230 employees from 15 different enterprises. The results showed that belief in a just world, interpersonal intelligence, and organizational citizenship behavior were significantly positively correlated. Interpersonal intelligence played a moderating role between belief in a just world and organizational citizenship behavior; the organizational citizenship behavior of individuals with high interpersonal intelligence increased with the strengthening of the belief in a just world, and this increase was larger than that experienced by individuals with low interpersonal intelligence. This meant that under a certain level of belief in a just world, a high level of interpersonal intelligence was more conducive to promoting employees' sustainable organizational citizenship behavior.

Religious and spiritual struggles are typically assessed by self-report scales using closed-ended items, yet nascent research suggests that using open-ended items may complement and advance assessment. In the current study, undergraduate participants (N = 976) completed open-ended descriptions of their religious and spiritual struggles, the Religious and Spiritual Struggles Scale (RSS), and a standardized measures of religious belief salience. Qualitative coding showed that the themes emerging from open-ended descriptions generally fell within the broad domains of the RSS though some descriptions reflected more contextualized struggles. Scores derived from the open-ended responses to assess RSS domains achieved evidence of reliability as well as convergent and discriminant validity with the RSS . Correlations revealed a mix of similar and divergent associations between methods of assessing religious and spiritual struggles and religious belief salience. Open-ended descriptions of religious and spiritual struggles may yield reliable and valid information that is related to but distinct from assessments relying on closed-ended items.

In all forms of folk beliefs, ancestor worship is a universal traditional belief form of the Vietnamese people. As a Vietnamese people, “everyone worships their ancestors, everyone worships their parents and grandparent”. Ancestor worship is a common belief in the whole country. It is a belief that expresses the deeply humanistic spirit of the Vietnamese people and has great values in human life. So, what is the nature of ancestor worship? What is the values of ancestor worship in life? And in the context of globalization, how has this the belief changed? This study focuses on analyzing the above contents, thereby highlighting the value of this belief in the spiritual life of Vietnamese people; to point out the positive and negative changes of this belief in the current period; from that, take the right measures to bring into play the positive and limit the negative side of those changes in the spiritual life of Vietnamese people.

The purpose of this study was to present the challenges faced when implementing a diet and exercise intervention for low-income older Hispanics with type 2 diabetes with an observational study of recruitment, attendance, and characteristics of Hispanic adults with type 2 diabetes in a community congregate meal site pre and post administration of a diet and exercise intervention. This report evaluates retentions and diabetes self-management beliefs Hispanic adults ≥60 years with type 2 diabetes (n=17) at baseline, and completion of the six-month intervention in terms of the Health Belief Model. There was limited interest in controlling diabetes with diet and exercise. Major barriers included lack of perceived vulnerability to diabetes complications and a belief that medication alone is sufficient to stabilize blood glucose. Environmental barriers included lack of transportation, access to exercise groups, access grocery stores, and limited ability to pay for healthy foods. A lesson learned from this intervention was that the diet and exercise intervention given was insufficient as a cue to action for this population interventions to engage low-income, older Hispanics with diabetes in diet and exercise need to consider strategies to overcome barriers such as health beliefs, transportation issues, lack of access to nutritious food and group exercise classes.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Echoing the long-held JTB account of knowledge, according to which knowledge can be conceptually analyzed as justified true belief, Backes (2019) argues that our epistemic aim is to believe truly or accurately and emphasizes on that “a belief is justified iff it is highly probable”. We maintain that this line of reasoning is deficient, in terms of epistemic concerns and non-epistemic concerns of interest for both philosophy and legal theory. Specifically, in this short paper, we argue for the ineffectiveness of the aforementioned Backes’ view to meet the challenges posed by the ongoing rapid techno-scientific transformation of our contemporary societies and ways-of life.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.